scholarly journals Modeling of sound propagation in the atmospheric boundary layer: Application of the MIUU mesoscale model

1999 ◽  
Vol 104 (D10) ◽  
pp. 11891-11901 ◽  
Author(s):  
L. R. Hole ◽  
H. M. Mohr
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Sound Propagation ◽  
Mesoscale Model

scholarly journals Mesoscale nesting interface of the PALM model system 6.0

2020 ◽  
Author(s):  
Eckhard Kadasch ◽  
Matthias Sühring ◽  
Tobias Gronemeier ◽  
Siegfried Raasch
Keyword(s):  
Boundary Layer ◽  
Boundary Conditions ◽  
Atmospheric Boundary Layer ◽  
Mesoscale Model ◽  
Model System ◽  
Convective Boundary ◽  
Convective Structures ◽  
Eddy Simulation ◽  
Synoptic Conditions ◽  
Turbulence Generation

Abstract. In this paper, we present a newly developed mesoscale nesting interface for the PALM model system 6.0, which enables PALM to simulate the atmospheric boundary layer under spatially heterogeneous and non-stationary synoptic conditions. The implemented nesting interface, which is currently tailored to the mesoscale model COSMO, consists of two major parts: (i) the preprocessor INIFOR, which provides initial and time-dependent boundary conditions from mesoscale model output and (ii) PALM's internal routines for reading the provided forcing data and superimposing synthetic turbulence to accelerate the transition to a fully developed turbulent atmospheric boundary layer. We describe in detail the conversion between the sets of prognostic variables, transformations between model coordinate systems, as well as data interpolation onto PALM's grid, which are carried out by INIFOR. Furthermore, we describe PALM's internal usage of the provided forcing data, which besides the temporal interpolation of boundary conditions and removal of any residual divergence includes the generation of stability-dependent synthetic turbulence at the inflow boundaries in order to accelerate the transition from the turbulence-free mesoscale solution to a resolved turbulent flow. We demonstrate and evaluate the nesting interface by means of a semi-idealized benchmark case. We carried out a large-eddy simulation (LES) of an evolving convective boundary layer on a clear-sky spring day. Besides verifying that changes in the inflow conditions enter into and successively propagate through the PALM domain, we focus our analysis on the effectiveness of the synthetic turbulence generation. By analysing various turbulence statistics, we show that the inflow in the present case is fully adjusted after having propagated for about 1.5 eddy turn-over times downstream, which corresponds well to other state-of-the-art methods for turbulence generation. Furthermore, we observe that numerical artefacts in the form of under-resolved convective structures in the mesoscale model enter the PALM domain, biasing the location of the turbulent up- and downdrafts in the LES. With these findings presented, we aim to verify the mesoscale nesting approach implemented in PALM, point out specific shortcomings, and build a baseline for future improvements and developments.

scholarly journals Mesoscale nesting interface of the PALM model system 6.0

2021 ◽  
Vol 14 (9) ◽  
pp. 5435-5465
Author(s):  
Eckhard Kadasch ◽  
Matthias Sühring ◽  
Tobias Gronemeier ◽  
Siegfried Raasch
Keyword(s):  
Boundary Layer ◽  
Boundary Conditions ◽  
Atmospheric Boundary Layer ◽  
Mesoscale Model ◽  
Model System ◽  
Convective Boundary ◽  
Convective Structures ◽  
Eddy Simulation ◽  
Synoptic Conditions ◽  
Turbulence Generation

Abstract. In this paper, we present a newly developed mesoscale nesting interface for the PALM model system 6.0, which enables PALM to simulate the atmospheric boundary layer under spatially heterogeneous and non-stationary synoptic conditions. The implemented nesting interface, which is currently tailored to the mesoscale model COSMO, consists of two major parts: (i) the preprocessor INIFOR (initialization and forcing), which provides initial and time-dependent boundary conditions from mesoscale model output, and (ii) PALM's internal routines for reading the provided forcing data and superimposing synthetic turbulence to accelerate the transition to a fully developed turbulent atmospheric boundary layer. We describe in detail the conversion between the sets of prognostic variables, transformations between model coordinate systems, as well as data interpolation onto PALM's grid, which are carried out by INIFOR. Furthermore, we describe PALM's internal usage of the provided forcing data, which, besides the temporal interpolation of boundary conditions and removal of any residual divergence, includes the generation of stability-dependent synthetic turbulence at the inflow boundaries in order to accelerate the transition from the turbulence-free mesoscale solution to a resolved turbulent flow. We demonstrate and evaluate the nesting interface by means of a semi-idealized benchmark case. We carried out a large-eddy simulation (LES) of an evolving convective boundary layer on a clear-sky spring day. Besides verifying that changes in the inflow conditions enter into and successively propagate through the PALM domain, we focus our analysis on the effectiveness of the synthetic turbulence generation. By analysing various turbulence statistics, we show that the inflow in the present case is fully adjusted after having propagated for about two to three eddy-turnover times downstream, which corresponds well to other state-of-the-art methods for turbulence generation. Furthermore, we observe that numerical artefacts in the form of grid-scale convective structures in the mesoscale model enter the PALM domain, biasing the location of the turbulent up- and downdrafts in the LES. With these findings presented, we aim to verify the mesoscale nesting approach implemented in PALM, point out specific shortcomings, and build a baseline for future improvements and developments.

scholarly journals A physics-informed neural network for sound propagation in the atmospheric boundary layer

2021 ◽  
Author(s):  
Chris Pettit ◽  
D. Wilson
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Loss Function ◽  
Sound Speed ◽  
Network Architecture ◽  
Sound Propagation ◽  
Transmission Loss ◽  
Training Data ◽  
Data Driven

We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.

Downwind sound propagation in an atmospheric boundary layer

AIAA Journal ◽  
1989 ◽  
Vol 27 (5) ◽  
pp. 515-523 ◽  
Author(s):  
William E. Zorumski ◽  
William L. Willshire
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Sound Propagation

scholarly journals Modeling of the heat island in the period of extreme frost in Moscow in January 2017

2019 ◽  
Vol 55 (5) ◽  
pp. 13-31
Author(s):  
V. P. Yushkov ◽  
M. M. Kurbatova ◽  
M. I. Varentsov ◽  
E. A. Lezina ◽  
G. A. Kurbatov ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Urban Heat Island ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Temperature Fields ◽  
Heat Island ◽  
Vertical Extent ◽  
Numerical Predictions ◽  
Urban Heat

Using the example of an analysis of an extreme lowering of temperature in Moscow in January 2017, the horizontal and vertical extent of the urban heat island against the background of a strong stable stratification of the atmospheric boundary layer is studied. The possibilities of measuring and monitoring the vertical structure of the atmosphere by means of ground-based remote sensing are investigated. The capabilities of the mesoscale model WRF, adapted for a detailed description of mixing processes in the atmospheric boundary layer, in reproducing the spatial dynamics of the temperature anomaly are demonstrated. The numerical estimates of the amplitude and vertical extent of the urban heat island are compared with the measurement accuracy and the total errors of the numerical predictions. Comparison of measurement data and numerical simulation results on the WRF model, using the example of a winter urban heat island in January 2017, showed that mesoscale synoptic models so far only capture the main features of the urban heat island. But deviations between model and observed temperature fields can reach 5 C.

A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part II: Application and Evaluation in a Mesoscale Meteorological Model

2007 ◽  
Vol 46 (9) ◽  
pp. 1396-1409 ◽  
Author(s):  
Jonathan E. Pleim
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Atmospheric Boundary Layer ◽  
Mesoscale Model ◽  
Evaluation Studies ◽  
Vertical Mixing ◽  
Model Performance ◽  
State University ◽  
Meteorological Model ◽  
Wind Profilers

Abstract A new combined local and nonlocal closure atmospheric boundary layer model called the Asymmetric Convective Model, version 2, (ACM2) was described and tested in one-dimensional form and was compared with large-eddy simulations and field data in Part I. Herein, the incorporation of the ACM2 into the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is described. Model simulations using the MM5 with the ACM2 are made for the summer of 2004 and evaluated through comparison with surface meteorological measurements, rawinsonde profile measurements, and observed planetary boundary layer (PBL) heights derived from radar wind profilers. Overall model performance is as good as or better than similar MM5 evaluation studies. The MM5 simulations with the ACM2 compare particularly well to PBL heights derived from radar wind profilers during the afternoon hours. The ACM2 is designed to simulate the vertical mixing of any modeled quantity realistically for both meteorological models and air quality models. The next step, to be described in a subsequent article, is to incorporate the ACM2 into the Community Multiscale Air Quality (CMAQ) model for testing and evaluation.

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